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http://dx.doi.org/10.1016/j.jvs.2009.01.060 | DOI Listing |
BMC Health Serv Res
January 2025
School of Humanities and Social Sciences, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, China.
Background: To address the health inequity caused by decentralized management, China has introduced a provincial pooling system for urban employees' basic medical insurance. This paper proposes a research framework to evaluate similar policies in different contexts. This paper adopts a mixed-methods approach to more comprehensively and precisely capture the causal effects of the policy.
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January 2025
Department of Health Policy and Management, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Family physician program is one of the effective reforms of the health system in Iran, but despite the implementation of this program in rural areas and the passage of ten years since its implementation in two provinces of Fars and Mazandaran, its implementation has faced problems. The aim of this study is to identify and prioritize implementation solutions related to the challenges of the family physician program in Iran.
Methods: This is a qualitative study using semi-structured interviews with 22 snowball-sampled experts and managers of basic health insurers to extract problems and executive solutions through coding and data analysis using Atlas Ti software and content analysis in the first stage.
Sci Rep
January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFSci Rep
January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
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